Deep learning automation of MEST-C classification in IgA nephropathy.

Journal: Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
PMID:

Abstract

BACKGROUND: Although the MEST-C classification is among the best prognostic tools in immunoglobulin A nephropathy (IgAN), it has a wide interobserver variability between specialized pathologists and others. Therefore we trained and evaluated a tool using a neural network to automate the MEST-C grading.

Authors

  • Adrien Jaugey
    Université de Bourgogne Franche comté, France.
  • Elise Marechal
    Department of Nephrology, CHU Dijon, France.
  • Georges Tarris
    Université de Bourgogne Franche comté, France.
  • Michel Paindavoine
    Université de Bourgogne Franche comté, France.
  • Laurent Martin
    Université de Bourgogne Franche comté, France.
  • Melchior Chabannes
    Department of Nephrology, CHU Besançon, Besançon, France.
  • Mathilde Funes de la Vega
    Department of Pathology, CHU Dijon, France.
  • Mélanie Chaintreuil
    Department of Nephrology, CHU Dijon, Dijon, France.
  • Coline Robier
    Department of Nephrology, CHU Dijon, Dijon, France.
  • Didier Ducloux
    Université de Bourgogne Franche comté, France.
  • Thomas Crepin
    Département de Néphrologie, Dialyse et Transplantation, CHU de Besançon, Besançon, France.
  • Sophie Felix
    Department of Pathology, CHU Besançon France.
  • Amélie Jacq
    Department of Nephrology, CHU Dijon, Dijon, France.
  • Doris Calmo
    Department of Nephrology, CHU Besançon, Besançon, France.
  • Claire Tinel
    Department of Microbiology, Immunology and Transplantation, Nephrology and Kidney Transplantation Research Group, KU Leuven, Leuven, Belgium.
  • Gilbert Zanetta
    Department of Nephrology, CHU Dijon, France.
  • Jean-Michel Rebibou
    Department of Nephrology, CHU Dijon, France.
  • Mathieu Legendre
    Department of Nephrology, CHU Dijon, France.